mobile edge cloud motivation, implementation, challenges · mobile edge cloud motivation,...
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Mobile Edge CloudMotivation, Implementation, Challenges
Motivation in 5G
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 3
Cloud Computing so farPublic Clouds
• Flexibility
• Varying degree of resoureces
• Scalability
• Blueprints allow repeated deployment
• Reliability
• Data-center uptime, fault recognition, backups and snapshots
• Distributed geographically
• Convenience
• Administration only on booked VMs, not on underlying infrastructure
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 4
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 5
Application
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 6
Use Cases - I
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 7
Use Cases - II
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 8
Use Cases - III
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 9
Use Cases - IV
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 10
Changes to Cloud Infrastructure?
Distributed
On-demand
Scaleable
Reliable
Distributed, but bigger spatial diversity
On-demand, but with faster deployment
Scaleable, but with even more devices
Reliable, but 5G compliant
Implementation
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 12
Key Technology
Mijumbi, Rashid, et al. "Network function virtualization: State-of-the-art and research challenges." IEEE Communications Surveys & Tutorials 18.1 (2016): 236-262.
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 13
KVM - I
• KVM is a virtualization infrastructure
• It uses the Linux Kernel as a hypervisor Type 2
— Part of kernel since 2007
• Benefits from hardware extensions: Intel VT-x, AMD-V
• QEMU (Quick EMUlator) provides virtualized hardware
Linux Kernel
OS
APP 1
VM
HARDWARE
OS
APP 2
VM
OS
APP 3
VM
OS
KVM
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 14
KVM - II
• QEMU (Quick EMUlator) provides virtualized hardware
• Libvirt is the API for lifecycle management
• Possible interfaces for VM management:virsh, virt-manager, Openstack
Linux+KVM
OS
APP 1
VM
HARDWARE
OS
QEMU
libvirt
virshvirt-manager
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 15
Migration General Structure
HYPERVISOR
OS
APP 1
VM
HARDWARE
OS
APP 2
VM
OS
APP 3
VM
OS
HYPERVISOR
OS
APP 1
HARDWARE
OS
APP 5
VM
OS
APP 6
VM
OS
HYPERVISOR
OS
APP 7
VM
HARDWARE
OS
APP 8
VM
OS
APP 9
VM
OS
Virtual Machine Manager
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 16
Reasons for Live Migration
Provider Perspective
• Load balancing
• In case of overloaded network
• Proactive fault tolerance
• If monitoring indicates imminent failure
• Power management
• Consolidation of VMs so hosts can be shut down
• Resource sharing
• In case of overloaded CPU or memory
• Online system maintenance
• Maintenance of hosts
Generally performance-centric
Our Perspective (5G Networks)
• MEC – Continuous Service Delivery
• The service has to be beneficial to the mobile device offloading, communication, etc.
• Stay in (close) proximity to device (for low latency requirements)
• Always stay in „the middle“ of communicating users/devices (e.g. social VR applications)
Generally latency-centric
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 17
Virtual Machine Memory Layout
Hu, Wenjin, et al. "A quantitative study of virtual machine live migration." Proceedings of the 2013 ACM cloud and autonomic computing conference. ACM, 2013.
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 18
Live Migration – Pre-copy
• Whole VM state is transmitted at the beginning
• Iteration: while running, dirty pages are resend
• i) until total amount of memory has been sent
• ii) number of iterations exceed previously set parameters
• iii) number of dirty pages below threshold
Choudhary, Anita, et al. "A critical survey of live virtual machine migration techniques." Journal of Cloud Computing 6.1 (2017): 23.
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 19
Live Migration – Post-copy
• Minimum VM state is transmitted at the beginning
• VM is started at destination
• Resend memory pages until all transferred
Choudhary, Anita, et al. "A critical survey of live virtual machine migration techniques." Journal of Cloud Computing 6.1 (2017): 23.
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 20
Virtual Machine Memory Layout
Hu, Wenjin, et al. "A quantitative study of virtual machine live migration." Proceedings of the 2013 ACM cloud and autonomic computing conference. ACM, 2013.
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 21
Virtual Machine Migration Times - Baseline
Hu, Wenjin, et al. "A quantitative study of virtual machine live migration." Proceedings of the 2013 ACM cloud and autonomic computing conference. ACM, 2013.
„Downtime represents the time that the VM being migrated was unresponsive to ping requests.“
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 22
Migration Scenarios
• Network intensive
• E.g. web server responding to HTTP requests
• Memory intensive
• E.g. Database server performing queries on in-memory database, memtest
• Storage intensive
• E.g. searching inside files (read intensive)
• Compute intensive
• E.g. calculation of Pi to nth digit, FFT calculations
• Any combination of these
• E.g. offloading for computer vision (network + computation)
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 23
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 24
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 25
Possible Use Cases
• Object recognition
• Computationally intensive
• For command and control latency-critical
• High bandwidth necessary when dealing with raw video data
• Might improve with low-latency video codecs
• Alex‘ research
• fast music
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 26
Computer Vision Testbed
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 27
Performance Comparison
Face Recognition through streaming to Intel NUC
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 28
Conclusions
• Previous research focusses on data centers with live migration
• Baseline migration time is already very fast
• Depending on the technology that is used
• Might speed up with Containers <-> VMs
• Further experiments must include apprioriate scenario
• Modify scenario to fit possible application
Challenges
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 30
Network
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 31
Data - I
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 32
So…separating Framework and State?
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 33
Data - II
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 34
So…distributed data?
NameTitleTechnische Universität Dresden, Deutsche Telekom Chair of Communication Networks
Slide 35
Thank you for your attention
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